import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
transform = transforms.Compose([
transforms.ToTensor(),
])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
# ==== VAE model ====
class VAE(nn.Module):
def __init__(self, input_dim=784, hidden_dim=400, latent_dim=20):
super(VAE, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc_mu = nn.Linear(hidden_dim, latent_dim)
self.fc_logvar = nn.Linear(hidden_dim, latent_dim)
self.fc2 = nn.Linear(latent_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, input_dim)
def encode(self, x):
h = torch.relu(self.fc1(x))
mu = self.fc_mu(h)
logvar = self.fc_logvar(h)
return mu, logvar
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
h = torch.relu(self.fc2(z))
return torch.sigmoid(self.fc3(h))
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
# ===== model training =====
model = VAE()
optimizer = optim.Adam(model.parameters(), lr=1e-3)
def loss_function(recon_x, x, mu, logvar):
BCE = nn.functional.binary_cross_entropy(recon_x, x, reduction='sum')
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return BCE + KLD
# ===== training loop =====
epochs = 5
for epoch in range(epochs):
model.train()
train_loss = 0
for batch_idx, (data, _) in enumerate(train_loader):
data = data.view(-1, 784) # Flatten the input
optimizer.zero_grad()
recon_batch, mu, logvar = model(data)
loss = loss_function(recon_batch, data, mu, logvar)
loss.backward()
train_loss += loss.item()
optimizer.step()
print(f'Epoch {epoch + 1}, Loss: {train_loss / len(train_loader.dataset):.4f}')
# ===== new sample creating =====
model.eval()
with torch.no_grad():
z = torch.randn(16, 20) # Generate random latent vectors
samples = model.decode(z).view(-1, 1, 28, 28) # Decode to images
$ python3 vae.py
Epoch 1, Loss: 164.1793
Epoch 2, Loss: 121.6226
Epoch 3, Loss: 114.7562
Epoch 4, Loss: 111.7122
Epoch 5, Loss: 109.9405
Loss がエポックごとに下がっている → 学習が進んでいる証拠
VAE の Loss は「再構築誤差 (BCE) ＋ KLダイバージェンス」なので、単純な分類モデルの Accuracy とは違って「小さくなるほど良い」という見方